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Predicted missing ratings using SVD algorithm from the Surprise Library for items from a file containing user ratings for multiple items by comparing a user’s ratings for available items with those of other user’s ratings and the project was built in Python

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Nehal-Pawar/Recommender-System

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Recommender-System

April 2018 – October 2019

• (Python) Predicted missing ratings by comparing a user’s ratings using SVD from the Surprise Library

• (Spark) Recommended movies by (Item Based Collaborative filtering) paring similar user rating

• (Mllib) Used spark ALS recommendation to predict missing ratings for movie lens dataset

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Predicted missing ratings using SVD algorithm from the Surprise Library for items from a file containing user ratings for multiple items by comparing a user’s ratings for available items with those of other user’s ratings and the project was built in Python

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python spark svd movielens-dataset datamining user-rating surprise-library predict-missing-ratings

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